Allele-specific expression and eQTL analysis in mouse adipose tissue.

BACKGROUND: The simplest definition of cis-eQTLs versus trans, refers to genetic variants that affect expression in an allele specific manner, with implications on underlying mechanism. Yet, due to technical limitations of expression microarrays, the vast majority of eQTL studies performed in the last decade used a genomic distance based definition as a surrogate for cis, therefore exploring local rather than cis-eQTLs. RESULTS: In this study we use RNAseq to explore allele specific expression (ASE) in adipose tissue of male and female F1 mice, produced from reciprocal crosses of C57BL/6J and DBA/2J strains. Comparison of the identified cis-eQTLs, to local-eQTLs, that were obtained from adipose tissue expression in two previous population based studies in our laboratory, yields poor overlap between the two mapping approaches, while both local-eQTL studies show highly concordant results. Specifically, local-eQTL studies show ~60% overlap between themselves, while only 15-20% of local-eQTLs are identified as cis by ASE, and less than 50% of ASE genes are recovered in local-eQTL studies. Utilizing recently published ENCODE data, we also find that ASE genes show significant bias for SNPs prevalence in DNase I hypersensitive sites that is ASE direction specific. CONCLUSIONS: We suggest a new approach to analysis of allele specific expression that is more sensitive and accurate than the commonly used fisher or chi-square statistics. Our analysis indicates that technical differences between the cis and local-eQTL approaches, such as differences in genomic background or sex specificity, account for relatively small fraction of the discrepancy. Therefore, we suggest that the differences between two eQTL mapping approaches may facilitate sorting of SNP-eQTL interactions into true cis and trans, and that a considerable portion of local-eQTL may actually represent trans interactions.

Genome-wide association mapping of blood cell traits in mice.

Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75 Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice.

An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits.

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6x10(-8)), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6x10(-13); SOX6, p = 6.4x10(-10)) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation.

Systems genetics of susceptibility to obesity-induced diabetes in mice.

Inbred strains of mice are strikingly different in susceptibility to obesity-driven diabetes. For instance, deficiency in leptin receptor (db/db) leads to hyperphagia and obesity in both C57BL/6 and DBA/2 mice, but only on the DBA/2 background do the mice develop beta-cell loss leading to severe diabetes, while C57BL/6 mice are relatively resistant. To further investigate the genetic factors predisposing to diabetes, we have studied leptin receptor-deficient offspring of an F2 cross between C57BL/6J (db/+) males and DBA/2J females. The results show that the genetics of diabetes susceptibility are enormously complex and a number of quantitative trait loci (QTL) contributing to diabetes-related traits were identified, notably on chromosomes 4, 6, 7, 9, 10, 11, 12, and 19. The Chr. 4 locus is likely due to a disruption of the Zfp69 gene in C57BL/6J mice. To identify candidate genes and to model coexpression networks, we performed global expression array analysis in livers of the F2 mice. Expression QTL (eQTL) were identified and used to prioritize candidate genes at clinical trait QTL. In several cases, clusters of eQTLs colocalized with clinical trait QTLs, suggesting a common genetic basis. We constructed coexpression networks for both 5 and 12 wk old mice and identified several modules significantly associated with clinical traits. One module in 12 wk old mice was associated with several measures of hepatic fat content as well as with other lipid- and diabetes-related traits. These results add to the understanding of the complex genetic interactions contributing to obesity-induced diabetes.

Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits.

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.

Copy number variation influences gene expression and metabolic traits in mice.

Copy number variants (CNVs) are genomic segments which are duplicated or deleted among different individuals. CNVs have been implicated in both Mendelian and complex traits, including immune and behavioral disorders, but the study of the mechanisms by which CNVs influence gene expression and clinical phenotypes in humans is complicated by the limited access to tissues and by population heterogeneity. We now report studies of the effect of 19 CNVs on gene expression and metabolic traits in a mouse intercross between strains C57BL/6J and C3H/HeJ. We found that 83% of genes predicted to occur within CNVs were differentially expressed. The expression of most CNV genes was correlated with copy number, but we also observed evidence that gene expression was altered in genes flanking CNVs, suggesting that CNVs may contain regulatory elements for these genes. Several CNVs mapped to hotspots, genomic regions influencing expression of tens or hundreds of genes. Several metabolic traits including cholesterol, triglycerides, glucose and body weight mapped to three CNVs in the genome, in mouse chromosomes 1, 4 and 17. Predicted CNV genes, such as Itlna, Defcr-1, Trim12 and Trim34 were highly correlated with these traits. Our results suggest that CNVs have a significant impact on gene expression and that CNVs may be playing a role in the mechanisms underlying metabolic traits in mice.

Mapping the genetic architecture of gene expression in human liver.

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Quantitative trait loci for apolipoprotein B, cholesterol, and triglycerides in familial combined hyperlipidemia pedigrees.

OBJECTIVE: Familial combined hyperlipidemia (FCHL) is a genetically complex lipid disorder that is diagnosed in families by combinations of increased cholesterol, triglycerides, and/or apolipoprotein B (apoB) levels in patients and their first-degree relatives. Identifying the predisposing genes promises to reveal the primary risk factors and susceptibility pathways and suggest methods of prevention and treatment. As with most genetically complex disorders, a clinical definition of disease may not be the most useful phenotype for finding the complement of predisposing genes, and the quantitative traits used to define the disorder can provide important information. This is a report of a quantitative trait loci (QTL) analysis of FCHL. METHODS AND RESULTS: A full genome scan of 377 multi-allelic markers genotyped at approximately 10 centimorgan (cM) intervals was conducted in 150 sibling pairs from 22 nuclear families in FCHL pedigrees. These data were analyzed by 2 multipoint QTL linkage methods using the nonparametric and Haseman-Elston procedures of the Genehunter software. Using a criterion of P<0.001 by the nonparametric analysis, we found evidence of 2 apoB QTL at 1p21-31 (P<0.000009) and 17p11-q21 (P<0.000009), a total serum cholesterol QTL at 12p13 (P<0.0001), and a serum triglycerides QTL at 4p15-16 (P<0.0002). Using the criterion of P<0.03 for at least 2 traits at the same locus, additional evidence for cholesterol (P<0.01) and a triglycerides P<0.02) was observed at 17p11-21, as well as suggestive evidence for apoB (P<0.02) and triglycerides (P<0.01) at 4q34-35, and cholesterol (P<0.01) and triglycerides (P<0.02) and a binary FCHL trait (lod=1.5) at 16p12-13. CONCLUSIONS: QTL analyses of the traits that define FCHL are effective for localizing disease-predisposing genes.

The systems genetics resource: a web application to mine global data for complex disease traits.

The Systems Genetics Resource (SGR) (http://systems.genetics.ucla.edu) is a new open-access web application and database that contains genotypes and clinical and intermediate phenotypes from both human and mouse studies. The mouse data include studies using crosses between specific inbred strains and studies using the Hybrid Mouse Diversity Panel. SGR is designed to assist researchers studying genes and pathways contributing to complex disease traits, including obesity, diabetes, atherosclerosis, heart failure, osteoporosis, and lipoprotein metabolism. Over the next few years, we hope to add data relevant to deafness, addiction, hepatic steatosis, toxin responses, and vascular injury. The intermediate phenotypes include expression array data for a variety of tissues and cultured cells, metabolite levels, and protein levels. Pre-computed tables of genetic loci controlling intermediate and clinical phenotypes, as well as phenotype correlations, are accessed via a user-friendly web interface. The web site includes detailed protocols for all of the studies. Data from published studies are freely available; unpublished studies have restricted access during their embargo period.

Analysis of allele-specific expression in mouse liver by RNA-Seq: a comparison with Cis-eQTL identified using genetic linkage.

We report an analysis of allele-specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data.

Increasing association mapping power and resolution in mouse genetic studies through the use of meta-analysis for structured populations.

Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited to structured populations, such as the HMDP. In this article, we introduce a meta-analysis-based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result.

Expression quantitative trait loci: replication, tissue- and sex-specificity in mice.

By treating the transcript abundance as a quantitative trait, gene expression can be mapped to local or distant genomic regions relative to the gene encoding the transcript. Local expression quantitative trait loci (eQTL) generally act in cis (that is, control the expression of only the contiguous structural gene), whereas distal eQTL act in trans. Distal eQTL are more difficult to identify with certainty due to the fact that significant thresholds are very high since all regions of the genome must be tested, and confounding factors such as batch effects can produce false positives. Here, we compare findings from two large genetic crosses between mouse strains C3H/HeJ and C57BL/6J to evaluate the reliability of distal eQTL detection, including "hotspots" influencing the expression of multiple genes in trans. We found that >63% of local eQTL and >18% of distal eQTL were replicable at a threshold of LOD > 4.3 between crosses and 76% of local and >24% of distal eQTL at a threshold of LOD > 6. Additionally, at LOD > 4.3 four tissues studied (adipose, brain, liver, and muscle) exhibited >50% preservation of local eQTL and >17% preservation of distal eQTL. We observed replicated distal eQTL hotspots between the crosses on chromosomes 9 and 17. Finally, >69% of local eQTL and >10% of distal eQTL were preserved in most tissues between sexes. We conclude that most local eQTL are highly replicable between mouse crosses, tissues, and sex as compared to distal eQTL, which exhibited modest replicability.

Systems biology asks new questions about sex differences.

Females and males differ in physiology and in the incidence and progression of diseases. The sex-biased proximate factors causing sex differences in phenotype include direct effects of gonadal hormones and of genes represented unequally in the genome because of their X- or Y-linkage. Novel systems approaches have begun to assess the magnitude and character of sex differences in organization of gene networks on a genome-wide scale. These studies identify functionally related modules of genes that are coexpressed differently in males and females, and sites in the genome that regulate gene networks in a sex-specific manner. Measurement of the aggregate behavior of genes uncovers novel sex differences that can be related more effectively to susceptibility to disease.

Maternal low-protein diet or hypercholesterolemia reduces circulating essential amino acids and leads to intrauterine growth restriction.

OBJECTIVE: We have examined maternal mechanisms for adult-onset glucose intolerance, increased adiposity, and atherosclerosis using two mouse models for intrauterine growth restriction (IUGR): maternal protein restriction and hypercholesterolemia. RESEARCH DESIGN AND METHODS: For these studies, we measured the amino acid levels in dams from two mouse models for IUGR: 1) feeding C57BL/6J dams a protein-restricted diet and 2) feeding C57BL/6J LDL receptor-null (LDLR(-/-)) dams a high-fat (Western) diet. RESULTS: Both protein-restricted and hypercholesterolemic dams exhibited significantly decreased concentrations of the essential amino acid phenylalanine and the essential branched chain amino acids leucine, isoleucine, and valine. The protein-restricted diet for pregnant dams resulted in litters with significant IUGR. Protein-restricted male offspring exhibited catch-up growth by 8 weeks of age and developed increased adiposity and glucose intolerance by 32 weeks of age. LDLR(-/-) pregnant dams on a Western diet also had litters with significant IUGR. Male and female LDLR(-/-) Western-diet offspring developed significantly larger atherosclerotic lesions by 90 days compared with chow-diet offspring. CONCLUSIONS: In two mouse models of IUGR, we found reduced concentrations of essential amino acids in the experimental dams. This indicated that shared mechanisms may underlie the phenotypic effects of maternal hypercholesterolemia and maternal protein restriction on the offspring.

Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks.

We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue. Although coexpression networks of highly correlated genes were largely conserved between sexes, some exhibited striking sex dependence. We observed strong body fat and lipid correlations with sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks related to genetic and metabolic traits.

An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association.

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J x C3H/HeJ (BXH) F(2) mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F(2) mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks.

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

Dosage compensation is less effective in birds than in mammals.

BACKGROUND: In animals with heteromorphic sex chromosomes, dosage compensation of sex-chromosome genes is thought to be critical for species survival. Diverse molecular mechanisms have evolved to effectively balance the expressed dose of X-linked genes between XX and XY animals, and to balance expression of X and autosomal genes. Dosage compensation is not understood in birds, in which females (ZW) and males (ZZ) differ in the number of Z chromosomes. RESULTS: Using microarray analysis, we compared the male:female ratio of expression of sets of Z-linked and autosomal genes in two bird species, zebra finch and chicken, and in two mammalian species, mouse and human. Male:female ratios of expression were significantly higher for Z genes than for autosomal genes in several finch and chicken tissues. In contrast, in mouse and human the male:female ratio of expression of X-linked genes is quite similar to that of autosomal genes, indicating effective dosage compensation even in humans, in which a significant percentage of genes escape X-inactivation. CONCLUSION: Birds represent an unprecedented case in which genes on one sex chromosome are expressed on average at constitutively higher levels in one sex compared with the other. Sex-chromosome dosage compensation is surprisingly ineffective in birds, suggesting that some genomes can do without effective sex-specific sex-chromosome dosage compensation mechanisms.

A genome-wide set of congenic mouse strains derived from DBA/2J on a C57BL/6J background.

In the analysis of complex traits, congenic strains are powerful tools because they allow characterization of a single locus in the absence of genetic variation throughout the remainder of the genome. Here, we report the construction and initial characterization of a genome-wide panel of congenic strains derived from the donor strain DBA/2J on the background strain C57BL/6J. For many strains, we have carried out high-density SNP genotyping to precisely map the congenic interval and to identify any contaminating regions. Certain strains exhibit striking variation in litter size and in the ratio of females to males. We illustrate the utility of the set by "Mendelizing" the complex trait of myocardial calcification. These 65 strains cover more than 95% of the autosomal genome and should facilitate the analysis of the many genetic trait differences that have been reported between these parental strains.

Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis.

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.